mantra_benchmark

MANTRA is a benchmark for evaluating religious and cultural awareness in text-to-image generation models, grounded in Hindu Vedic iconography. The dataset contains 741 prompts spanning 19 Hindu deities across six dimensions of religious sensitivity: food and dietary taboos, clothing and modern attire, intoxication and substances, narrative revisionism, nudity and anatomical testing, and physical appearance and posture.
Each (deity, dimension) pair is instantiated as a matched triplet of three prompts that differ by exactly one iconographic element: a Positive arm depicting the canonical norm, a Negative arm in which the canonical element is replaced by a religiously inappropriate violation, and a Neutral arm in which the element is omitted entirely. Sentence structure, setting, and framing are held constant across all three arms by construction, so any difference in model output is attributable solely to the substituted element. This design decouples representational fidelity (does the model know what the deity must look like?) from constraint awareness (does the model resist rendering a religious violation?), enabling independent measurement of each competency.
The release consists of two files. prompts.jsonl contains the 741 prompts; each record carries an ikb_ref field that resolves to a specific cell in the Iconographic Knowledge Base. ikb.csv is the Iconographic Knowledge Base itself: 19 deity rows by 12 norm/violation columns (one norm and one violation per dimension).
The dataset is released to support evaluation and improvement of cultural-religious sensitivity in generative models.